The disclosure relates generally to devices for applying stimulation of organs for therapeutic purposes.
It is applicable to “active implantable medical devices” as defined by the Directive 90/385/EEC of 20 Jun. 1990 of the Council of the European Communities. This includes in devices for continuously monitoring heart activity and for delivering, electrical stimulation, resynchronization and/or defibrillation pulses to the heart, in the event of a rhythm disorder detected by the device. It also includes neurological devices, cochlear implants, etc., as well as devices for pH or other intracorporeal parameter measurements.
Regardless of the pathology to be treated, pacing therapy is usually done to maximize therapeutic effects, while minimizing side effects, and the energy consumption in the case where the stimulation is implemented in an autonomous implantable device.
This stimulation may take into account the dynamics of the pathology resulting for example from such an alteration of the autonomic nervous system (ANS), from cardiac or ANS remodeling, as well as from the therapy response (habituation, changes in the electrode-vagus nerve coupling in the case of vagus nerve stimulation (VNS)).
Thus, the application of optimal stimulation is complex, currently addressed imperfectly, although the application of optimal stimulation represents one of the highest priorities in the progress of neurostimulation.
At present, approaches to control of the stimulation in a closed loop can be classified into two families:    1) rules-based approaches, such as those described in U.S. Pat. Nos. 7,783,349 B2, 7,509,166 B2, US 2012/245656 A1 or WO 2011/137029 A; or    2) approaches based on a linear or non-linear transfer function of control variables, such as those described for example in EP 1102607 A1.
The first approach has two major limitations: i) it is difficult to define optimal rules for a given patient and ii) these rules are based on the definition of thresholds, which vary depending on the inter-patient and intra-patient variability.
The main limitations of the second approach are i) the computational complexity required to implement the controller, and ii) the amount of data required to adjust the parameters of the controller.
There are also other methods to estimate a set of rules specific to patients [1,2], to infer inter-patient or intra-patient adjustments [3], and to reduce the complexity of the calculations to be performed in the controller [4, 5]. However, these approaches remain theoretical and difficult to implement and integrate into active implantable devices with limited digital processing power.
Relevant References are:
    [1] A. Aarabi, and H. Bin, “Seizure prediction in intracranial EEG: A patient-specific rule-based approach”, Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, 2566-2569, Boston, Mass.;    [2] I. Capel, M. Rigla, G. Garcia-Sáez, A. Rodríguez-Herrero, B. Pons, D. Subias, F. Garcia-García, M. Gallach, M. Aguilar, C. Pérez-Gandía, E. J. Gómez, A. Caixàs, and M. E. Hernando, “Artificial Pancreas Using a Personalized Rule-Based Controller Achieves Overnight Normoglycemia in Patients with Type 1 Diabetes”, Diabetes Technol Ther., Vol. 16(3): 172-179 (2014);    [3] F. Porée, A. Kachenoura, G. Carrault, R. D. Molin, P. Mabo, and A. I. Hernández, “Surface Electrocardiogram Reconstruction From Intracardiac Electrograms Using a Dynamic Time Delay Artificial Neural Network”, Biomedical Engineering, IEEE Transactions on, Vol. 60, 106-114, (2013).    [4] H. M. Romero Ugalde, J.-C. Carmona, V. M. Alvarado and J. Reyes-Reyes, “Neural Network Design and Model Reduction Approach for Black Box Non Linear System Identification with Reduced Number of Parameters”, Neurocomputing, Vol. 101, 170-180 (2013).    [5] M. Lohning, M. Reble, J. Hasenauer, S. Yu, F. Allgower, “Model predictive control using reduced order models: Guaranteed stability for constrained linear systems”, Journal of Process Control, Vol. 24 (11), 1647-1659, (2014).
Finally, another major limitation of the existing approaches is related to the time constant of the closed loop control. It is usually fixed to a single predefined time scale (e.g. every heartbeat, every minute, every day, etc.), particularly in the case where the controlled variables are the results of the processes that are intermingled on different time scales, as in physiology.
The present disclosure aims to overcome these limitations of the prior art and to provide a stimulation control that requires only limited calculation, while being extremely flexible and able to very finely adjust stimulation to the observed physical and/or physiological situation.